L09 Themes

Data Visualization (STAT 302)

Overview

The goal of this lab is to play around with the theme options in ggplot2.

Datasets

We’ll be using the cdc.txt and NU_admission_data.csv datasets.

Code

# load package(s)

library(tidyverse)
library(janitor)
library(patchwork)
library(showtext)




# read in the cdc dataset
cdc <- read_delim(file = "data/cdc.txt", delim = "|") %>%
  mutate(
    genhlth = factor(
      genhlth,
      levels = c("excellent", "very good", "good", "fair", "poor"),
      labels = c("Excellent", "Very Good", "Good", "Fair", "Poor")
      )
    )

# read in NU admission data
admin_data <- read.csv("data/NU_admission_data.csv") %>%
  clean_names()

# set seed
set.seed(2468)

# selecting a random subset of size 100
cdc_small <- cdc %>% 
  slice_sample(n = 100)

Exercise 1

Use the cdc_small dataset to explore several pre-set ggthemes. The code below constructs the familiar scatterplot of weight by height and stores it in plot_01. Display plot_01 to observe the default theme. Explore/apply, and display at least 7 other pre-set themes from the ggplot2 or ggthemes package. Don’t worry about making adjustments to the figures under the new themes. Just get a sense of what the themes are doing to the original figure plot_01.

There should be at least 8 plots for this task, plot_01 is pictured below. Use patchwork or cowplot in combination with R yaml chunk options fig-height and fig-width (out-width and fig-align may be useful as well) to setup the 8 plots together in a user friendly arrangement.

Solution

Important

Which theme or themes do you particularly like? Why?

Solution

I personally like theme_grey(), and theme_classic(). For a visualization like this, I think the grey background provides a helpful contrast to the colored shapes while alos clearly outlining the legend in the bottom right corner. I like that the classic theme is not very distracting. Removing the lines allows the reader to view the data without noise created by grid lines. Additionally, I appreciate that the axes are clearly marked with bold lines which draws the reader’s attention to the scales.

Exercise 2

Using plot_01 from Exercise 1 and the theme() function, attempt to construct the ugliest plot possible (example pictured below). Be creative! It should NOT look exactly like the example. Since the goal is to understand a variety of adjustments, you should use a minimum of 10 different manual adjustments within theme().

Solution

Code

plot_01 +
  theme(
    axis.text.y = element_text(color = "blue", angle = 180),
    axis.text.x = element_text(color = "red", angle = -30, vjust = 1, hjust = 0), 
   panel.grid.major.x = element_line(color = "yellow", linewidth = 3),
   panel.grid.major.y = element_line(color = "red", linewidth = 3),
   plot.title = element_text(face = "italic", colour = "blue", size = 25),
   plot.background = element_rect(colour = "purple", fill = "pink", linewidth = 3),
   panel.background = element_rect(fill = "lightgreen"),
   legend.background = element_rect(
    fill = "lightyellow", 
    colour = "black", 
    linewidth = 3),
   legend.text = element_text(size = 10, face = "italic"),
  legend.title = element_text(size = 15, face = "bold"),
  axis.ticks.length = unit(2, "cm"),
  axis.title = element_text(color= "purple", face = "bold"),
   )

Exercise 3

We will be making use of your code from Exercise 3 from L07 Layers. Using the NU_admission_data.csv you created two separate plots derived from the single plot depicted in undergraduate-admissions-statistics.pdf. Style these plots so they follow a “Northwestern” theme. You are welcome to display the plots separately OR design a layout that displays both together (likely one stacked above the other).

Check out the following webpages to help create your Northwestern theme:

• Visual Identity
• Color Palettes
• Fonts & Typography — Need to use substitute fonts

Additional requirement:

Use a free non-standard font from google for the title. Pick one that looks similar to a Northwestern font.

Note

I find this blog post to be extremely useful for adding fonts. Important packages for using non-standard fonts are showtext, extrafont, extrafontdb, and sysfonts. The last 3 generally just need to be installed (not loaded per session).

Solution

Code

font_add_google("Arbutus Slab", "slab")
showtext_auto()


# data wrangling
bar_data <- admin_data %>%
  select(-contains("_rate")) %>%
    pivot_longer(
      cols = -year,
      names_to = "category",
      values_to = "value"
    ) %>%
    mutate(
      bar_label = prettyNum(value, big.mark = ",")
    )

# barplot
barplot <- ggplot(data = bar_data, aes(x = year, y = value, fill = category)) +
  geom_col(
    position = "identity", 
    width = 0.8
    ) +
   geom_text(
     aes(label = bar_label),
     color = "white",
     size = 2,
     vjust = 1,
     nudge_y = -300
    ) +
  scale_x_continuous(
    name = "Entering Year",
    breaks = 1999:2020, 
    expand = c(0, 0.25),
  ) +
  scale_y_continuous(
    name = "Applications", 
    breaks = seq(0, 50000, 5000),
    expand = c(0, 0), 
    limits = c(0, 50000),
    labels = scales::label_comma()
  ) +
  scale_fill_manual(
    name = NULL,
    limits = c("matriculants", "admitted_students", "applications"),
    values = c("#4E2A84", "#836EAA", "#B6ACD1"),
    labels = c("Matriculants", "Admiited Students", "Applications"),
  ) +
  theme_classic() +
  theme(
    legend.justification = c(0.5,1),
    legend.position = c(0.5,1),
    legend.direction = "horizontal",
    plot.title = element_text(hjust = 0.5, color = "#624D91", family = "slab", face = "bold", size = 25),
    plot.subtitle = element_text(hjust = 0.5, color = "#9F94BE", family = "slab", size = 18)
  ) +
  labs(title = "Northwestern University",
       subtitle = "Undergraduate Admissions 1999-2000"
       )

Code

# data wrangling
rate_data <- admin_data %>%
  select(year, contains("_rate")) %>%
    pivot_longer(
      cols = -year,
      names_to = "rate_type",
      values_to = "value"
    ) %>%
    mutate(
      rate_label = str_c(value, "%"),
      label_y = case_when(
        rate_type == "yield_rate" ~ value + 2,
        rate_type == "admission_rate" ~ value - 2
      )
    )

# rate plot
lineplot <- ggplot(data = rate_data, aes(x = year, y = value)) +
  geom_line(
    aes(color = rate_type)
    ) +
  geom_point(
    aes(color = rate_type, shape = rate_type)
  ) +
   geom_text(
     aes(y = label_y, 
         label = rate_label
         ),
     size = 2
    ) +
  scale_x_continuous(
    name = "Entering Year",
    breaks = 1999:2020, 
    expand = c(0, 0.3)
  ) +
  scale_y_continuous(
    name = "Rate", 
    expand = c(0, 0), 
    limits = c(0, 60),
    labels = scales::label_percent(scale = 1),
    position = "right"
  ) +
  scale_shape_manual(
    name = NULL, 
    labels = c("Admission Rate", "Yield Rate"),
    values = c(15, 8)
  ) +
  scale_color_manual(
    name = NULL,
    labels = c("Admission Rate", "Yield Rate"),
    values = c("#9F94BE", "#624d91" )) + 
  theme_classic() +
  theme(
    legend.justification = c(0.5,1),
    legend.position = c(0.5,1),
    legend.direction = "horizontal"
  ) 

Code

combined_plot <- barplot / lineplot

combined_plot

Challenge

Challenge is optional for all students, but we recommend trying them out!

Using cdc_small dataset, re-create your own version inspired by the plot below.

Must haves:

• Use two non-standard fonts (one for labeling the point and the other for the axes)
• Use at least two colors (one for the added point, another for the rest of the points)
• A curved arrow used to label the point

Using Bilbo Baggins’ responses below to the CDC BRSFF questions, add Bilbo’s data point to a scatterplot of weight by height.

• genhlth - How would you rate your general health? fair
• exerany - Have you exercised in the past month? 1=yes
• hlthplan - Do you have some form of health coverage? 0=no
• smoke100 - Have you smoked at least 100 cigarettes in your life time? 1=yes
• height - height in inches: 46
• weight - weight in pounds: 120
• wtdesire - weight desired in pounds: 120
• age - in years: 45
• gender - m for males and f for females: m

Note

Adding non-standard fonts can be an adventure. I find this blog post to be extremely useful for adding fonts. Important packages for using non-standard fonts are showtext, extrafont, extrafontdb, and sysfonts. The last 3 generally just need to be installed (not loaded per session).

Hint:

• Create a new dataset (maybe call it bilbo or bilbo_baggins) using either data.frame() (base R - example in book) or tibble() (tidyverse - see help documentation for the function). Make sure to use variable names that exactly match cdc’s variable names. We have provided the tidyverse approach.
• Search google fonts to find some free fonts to use (can get free fonts from other locations)

Solution

Code

# build dataset for Bilbo Baggins
bilbo <- tibble(
  genhlth  = "fair",
  exerany  = 1,
  hlthplan = 0,
  smoke100 = 1,
  height   = 46,
  weight   = 120,
  wtdesire = 120,
  age      = 45,
  gender   = "m"
)